과제정보
This research was supported by Korea Institute of Oriental Medicine (KSN1812181) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2021R1F1A1046705).
참고문헌
- Chae Y, Chang DS, Lee SH, Jung WM, Lee IS, Jackson S, et al. Inserting needles into the body: a meta-analysis of brain activity associated with acupuncture needle stimulation. J Pain. 2013;14(3):215-22. https://doi.org/10.1016/j.jpain.2012.11.011
- Chae Y. Past, present, and the future of understanding the entity of the meridian system. J Physiol & Pathol Korean Med. 2016;30(6):402-11. https://doi.org/10.15188/kjopp.2016.12.30.6.402
- Jeon SH, Lee IS, Lee H, Chae Y. A bibliometric analysis of acupuncture research trends in clinical trials. Kor J Acu. 2019;36(4):281-91.
- Kye K, Kim B. A study on methods of acupuncture points combination and hierarchy concept of acupuncture prescription in Korean medicine. J Korean Med. 2020;41(3):9-21.
- Kim M, Han CH. A survey on the educational status of basic Korean medicine and basic medical science in colleges of Korean medicine in 2020. J Korean Med. 2020;41(3):98-124. https://doi.org/10.13048/jkm.20028
- Park IS, Jung WM, Lee YS, Hahm DH, Park HJ, Chae Y. Characterization of Five-Shu acupoint pattern in Saam acupuncture using text mining. Kor J Acu. 2015;32(2):66-74.
- Choi DH, Lee SY, Lee IS, Ryu Y, Chae Y. Characteristics of Source acupoints: data mining of clinical trials database. Kor J Acu. 2021;38(2):100-9.
- Chu X, Sun B, Huang Q, Peng S, Zhou Y, Zhang Y. Quantitative knowledge presentation models of traditional Chinese medicine (TCM): A review. Artif Intell Med. 2020;103:101810. https://doi.org/10.1016/j.artmed.2020.101810
- Wang Y, Shi X, Li L, Efferth T, Shang D. The Impact of Artificial Intelligence on Traditional Chinese Medicine. Am J Chin Med. 2021;49(6):1297-314. https://doi.org/10.1142/S0192415X21500622
- Hwang YC, Lee IS, Ryu Y, Lee MS, Chae Y. Exploring traditional acupuncture point selection patterns for pain control: data mining of randomised controlled clinical trials. Acupunct Med. 2020:964528420926173.
- Su ZW, Ren YL, Zhou SY, Qin HZ, Chen DS, Liu T, et al. Analysis on characteristics of meridians and acupoints of acupuncture and moxibustion for diarrhea in ancient based on data mining. Zhongguo Zhen Jiu. 2013;33(10):905-9.
- Lu L, Wen Q, Hao X, Zheng Q, Li Y, Li N. Acupoints for Tension-Type Headache: A Literature Study Based on Data Mining Technology. Evid Based Complement Alternat Med. 2021;2021:5567697.
- Lee IS, Jo HJ, Lee SH, Jung WM, Kim SY, Park HJ, et al. Systematic review of selection of acupuncture points for lower back pain. Kor J Acu. 2012;29(4):519-36.
- Lee IS, Lee SH, Kim SY, Lee H, Park HJ, Chae Y. Visualization of the Meridian System Based on Biomedical Information about Acupuncture Treatment. Evid Based Complement Alternat Med. 2013;2013:872142.
- Mou ZJ, He LY, Song HJ, Cheng Q, Liu BY. Rule of point selection in treatment of cerebral palsy in children with acupuncture based on data mining of 1584 electronic medical records. Zhongguo Zhen Jiu. 2021;41(3):355-8.
- Liang J, Han MY, Wang CB, Lu XL, Sun ZR, Yin HN. Research progress in the integration of machine learning and acupunctology. Zhen Ci Yan Jiu. 2021;46(6):460-3.
- Jung WM, Park IS, Lee YS, Kim CE, Lee H, Hahm DH, et al. Characterization of hidden rules linking symptoms and selection of acupoint using an artificial neural network model. Front Med. 2019;13(1):112-20. https://doi.org/10.1007/s11684-017-0582-z
- Poldrack RA. Can cognitive processes be inferred from neuroimaging data? Trends Cogn Sci. 2006;10(2):59-63. https://doi.org/10.1016/j.tics.2005.12.004
- Lee YS, Ryu Y, Yoon DE, Kim CH, Hong G, Hwang YC, et al. Commonality and Specificity of Acupuncture Point Selections. Evid Based Complement Alternat Med. 2020;2020:2948292.
- Hwang YC, Lee YS, Ryu Y, Lee IS, Chae Y. Statistical inference of acupoint specificity: forward and reverse inference. Integr Med Res. 2020;9(1):17-20. https://doi.org/10.1016/j.imr.2020.01.005
- Sun QH, Li TT, Huang MT, Wang MY, Xiao X, Bai XH. Acupoint selection rules in treating gastroesophageal reflux disease with acupuncture in China based on data mining. Zhongguo Zhen Jiu. 2020;40(12):1374-8.
- Yu J, Jiang Y, Tu M, Liao B, Fang J. Investigating Prescriptions and Mechanisms of Acupuncture for Chronic Stable Angina Pectoris: An Association Rule Mining and Network Analysis Study. Evid Based Complement Alternat Med. 2020;2020:1931839.
- Yu S, Yang J, Yang M, Gao Y, Chen J, Ren Y, et al. Application of acupoints and meridians for the treatment of primary dysmenorrhea: a data mining-based literature study. Evid Based Complement Alternat Med. 2015;2015:752194.
- Hwang YC, Lee IS, Ryu Y, Lee YS, Chae Y. Identification of Acupoint Indication from Reverse Inference: Data Mining of Randomized Controlled Clinical Trials. J Clin Med. 2020;9(9).
- Chae Y, Ryu Y, Jung WM. An analysis of Indications of Meridians in DongUiBoGam using data mining. Kor J Acu. 2019;36(4):292-9.
- Jung WM, Lee T, Lee IS, Kim S, Jang H, Kim SY, et al. Spatial Patterns of the Indications of Acupoints Using Data Mining in Classic Medical Text: A Possible Visualization of the Meridian System. Evid Based Complement Alternat Med. 2015;2015:457071.
- Zhou X, Chen S, Liu B, Zhang R, Wang Y, Li P, et al. Development of traditional Chinese medicine clinical data warehouse for medical knowledge discovery and decision support. Artif Intell Med. 2010;48(2-3):139-52. https://doi.org/10.1016/j.artmed.2009.07.012
- Ren YL, Zeng F, Zhao L, Yang J, Liang FR. [Considerations about developing a clinical decision support system for evidence-based diagnosis and treatment of acupuncture-moxibustion]. Zhen Ci Yan Jiu. 2009;34(5):349-52.
- Jung WM, Chae Y, Jang BH. Development of Markup Language for Medical Record Charting: A Charting Language. Stud Health Technol Inform. 2015;216:879.
- Jung WM, Lee SH, Lee YS, Chae Y. Exploring spatial patterns of acupoint indications from clinical data: A STROBE-compliant article. Medicine (Baltimore). 2017;96(17):e6768. https://doi.org/10.1097/md.0000000000006768